scholarly journals A TP53-associated immune prognostic signature for the prediction of the overall survival and therapeutic responses in pancreatic cancer

2021 ◽  
Vol 19 (1) ◽  
pp. 191-208
Author(s):  
Yi Liu ◽  
◽  
Long Cheng ◽  
Xiangyang Song ◽  
Chao Li ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Tp53 Mutation ◽  
Prolactin Receptor ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Large Sample Size ◽  
Sequencing Data

<abstract> <p>Pancreatic cancer (PC) is a highly fatal disease correlated with an inferior prognosis. The tumor protein p53 (TP53) is one of the frequent mutant genes in PC and has been implicated in prognosis. We collected somatic mutation data, RNA sequencing data, and clinical information of PC samples in the Cancer Genome Atlas (TCGA) database. TP53 mutation was an independent prognostic predictor of PC patients. According to TP53 status, Gene set enrichment analysis (GSEA) suggested that TP53 mutations were related to the immunophenotype of pancreatic cancer. We identified 102 differentially expressed immune genes (DEIGs) based on TP53 mutation status and developed a TP53-associated immune prognostic model (TIPM), including Epiregulin (EREG) and Prolactin receptor (PRLR). TIPM identified the high-risk group with poor outcomes and more significant response potential to cisplatin, gemcitabine, and paclitaxel therapies. And we verified the TIPM in the International Cancer Genome Consortium (ICGC) cohort (PACA-AU) and Gene Expression Omnibus (GEO) cohort (GSE78229 and GSE28735). Finally, we developed a nomogram that reliably predicts overall survival in PC patients on the bias of TIPM and other clinicopathological factors. Our study indicates that the TIPM derived from TP53 mutation patterns might be an underlying prognostic therapeutic target. But more comprehensive researches with a large sample size is necessary to confirm the potential.</p> </abstract>

scholarly journals Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Kailun Zhou ◽  
Jincai Wu ◽  
Zhensheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Immune Cells ◽  
Prognostic Value ◽  
Tumor Tissue ◽  
Clinical Stage ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Clinical Prognosis

Abstract Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

scholarly journals Integrative analysis of mRNA and miRNA sequencing data for gliomas of various grades

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Dmitry Y. Gvaldin ◽  
Anton A. Pushkin ◽  
Nataliya N. Timoshkina ◽  
Eduard E. Rostorguev ◽  
Arbi M. Nalgiev ◽  
...  
Keyword(s):  
Overall Survival ◽  
Mirna Expression ◽  
Interaction Analysis ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Differential Analysis ◽  
Sequencing Data ◽  
Aberrant Expression

Abstract Background The purpose of this study was to characterize subtype-specific patterns of mRNA and miRNA expression of gliomas using The Cancer Genome Atlas (TCGA) data to search for genetic determinants that predict prognosis in terms of overall survival and to create interaction networks for grade 2 and 3 (G2 and G3) astrocytomas, oligodendrogliomas and grade 4 (G4) glioblastoma multiforme. Based on open-access TCGA data, 5 groups were formed: astrocytoma G2 (n = 58), astrocytoma G3 (n = 128), oligodendroglioma G2 (n = 102), oligodendroglioma G3 (n = 72) and glioblastoma G4 (n = 564); normal samples of brain tissue were also analysed (n = 15). Data of patient age, sex, survival and expression patterns of mRNA and miRNA were extracted for each sample. After stratification of the data into groups, a differential analysis of expression was carried out, genes and miRNAs that affect overall survival were identified and gene set enrichment analysis (GSEA) and interaction analysis were performed. Results A total of 939 samples of glial tumours were analysed, for which subtype-specific expression profiles of genes and miRNAs were identified and networks of mRNA-miRNA interactions were constructed. Genes whose aberrant expression level was associated with survival were determined, and pairwise correlations between differential gene expression (DEG) and differential miRNA expression (DE miRNA) were calculated. Conclusions The developed panel of genes and miRNAs allowed us to differentiate glioma subtypes and evaluate prognosis in terms of the overall survival of patients. The regulatory miRNA-mRNA pairs unique to the five glioma subtypes identified in this study can stimulate the development of new therapeutic approaches based on subtype-specific mechanisms of oncogenesis.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract Background Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aimed to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.Methods Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis and the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. Then the Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature and the CIBERSORT was used for estimating the fractions of immune cell types.Results A total of 397 hypoxia-related DEGs were detected and three genes (PDSS1, CDCA8 and SLC7A11) were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response and the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.Conclusions Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals Identification of RB1 Gene as a Immune-Related Prognostic Signature Based on Tumor Mutation Burdens in Bladder Cancer

2021 ◽  
Author(s):  
Ming Chen ◽  
Hong Cheng ◽  
Tiange Wu ◽  
Zeping Gui ◽  
Ying Gao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Poor Overall Survival ◽  
Mutant Type

Abstract Background: Bladder cancer (BC) is known as the eleventh most common malignant tumor all over the world, for either males or females. Developing effective regimens targeting more promising biomarkers aiming for better prognosis are required. Immune checkpoint inhibitors (ICI) have been demonstrated as a prospective and practical means to resist cancers. Theoretically, adequate infiltration of immune cells indicates more immunotherapy targets and may promise better prognosis.Methods: Full transcriptome data (n=433), clinical information (n=581) and mutation sequencing (n=412) were obtained freely from The Cancer Genome Atlas and independent mutation sequencing data of 101 samples were acquired from International Cancer Genome Consortium. Statistical processing was conducted using R packages with R x64 4.0.2. Gene biologically functional research was performed with gene set enrichment analysis (GSEA) based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 22 types of immune cell infiltration were assessed and calculated in 398 samples of BC tumors.Results: Tumor mutation burdens (TMB) of mutant type groups were higher than wild type groups for 19 genes, except for FGFR3 and CREBBP verifying that genomic mutation associates positively with TMB in BC tumor. Kaplan-Meier analysis showed high mutation frequency on RB1 had a negative effect on prognosis of BC patients and RB1 was an independent prognostic factor (p=0.004, HR=1.776) in BC. It was also demonstrated that RB1 mainly participate in singling pathways of cell proliferation and cell cycle. Proportions and correlation of 22 types of immune cells in 433 samples were determined. Immune cells with similar function are inclined to co-exist in tumor microenvironment of BC. Among them, regulatory T cells (Tregs) were detected as a negatively correlated type immune cell to mutation of RB1 that probably increases the incidence of tumor immune escaping in BC.Conclusion: RB1 can be identified as an independent prognostic predictor, and there is a chance for contribution to poor overall survival as the mutation occurs. What's more, mutation of RB1 also functions as a biomarker that represses the infiltration of Tregs, increasing the incidence of tumor immune escaping in BC.

scholarly journals Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Lymph Node ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer.

scholarly journals Autophagy-related long non-coding RNA signature for ovarian cancer

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052097076
Author(s):  
Chan Meng ◽  
Jie-Qiong Zhou ◽  
Yong-Sheng Liao
Keyword(s):  
Ovarian Cancer ◽  
Enrichment Analysis ◽  
The United States ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Rna Molecules ◽  
Diagnosis And Prognosis ◽  
Specific Tumor

Objective Ovarian cancer (OC) affects nearly 22,000 women annually in the United States and ranks fifth in cancer deaths, largely because of being diagnosed at an advanced stage. Autophagy is the cellular process of self-degrading damaged or degenerate proteins and organelles. Long non-coding RNAs (lncRNAs) are a group of RNA molecules whose transcripts are greater than 200 nt but are not translated into proteins. However, just a small number of autophagy-related lncRNAs have been explored in depth. Methods We used RNA sequencing data from The Cancer Genome Atlas (TCGA) and autophagy datasets to identify dysfunctional autophagy-related lncRNAs and provide potential useful biomarkers for OC diagnosis and prognosis. Results Seventeen differentially expressed lncRNAs (AC010186.3, AC006001.2, LBX2-AS1, SNHG17, AC011445.1, AC083880.1, MIR193BHG, AC025259.3, HCG14, AC007114.1, AC108673.2, USP30-AS1, AC010336.5, LINC01132, AC006333.2, LINC00665 and AC027348.1) were selected as independent prognostic factors for OC patients. Functional annotation of the data was performed through gene set enrichment analysis (GSEA). The results suggested that the high-risk group was mainly enriched in specific tumor-related and metabolism pathways. Conclusion Based on the online databases, we identified novel autophagy-related lncRNAs for the prognosis of ovarian cancer.

scholarly journals Ferroptosis-Related IncRNAs Are Prognostic Biomarker of Overall Survival in Pancreatic Cancer Patients

2021 ◽  
Author(s):  
Dongjie Chen ◽  
Wenzhe Gao ◽  
Longjun Zang ◽  
Xianlin Zhang ◽  
Zheng Li ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Pearson Correlation ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Validation Dataset ◽  
Primary Therapy ◽  
Cerna Network ◽  
Cox Analysis

Abstract Background: Pancreatic cancer (PC) is one of the most lethal malignancies, the mortality and morbidity of which have been increasing over the past decade. Ferroptosis, a newly identified iron-dependent pattern of cell death, can be induced by iron chelators and small lipophilic antioxidants. Nonetheless, the prognostic significance of ferroptosis-related lncRNAs in PC remains to be explicated. Methods: We obtained the lncRNA expression matrix and clinicopathological information of PC patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets in the current study. Pearson correlation analysis was conducted to delve into the ferroptosis-related lncRNAs, and univariate Cox analysis was implemented to examine the prognostic values in PC patients. The least absolute shrinkage and selection operator Cox (LASSO-Cox) analysis was performed to establish a ferroptosis-related lncRNA prognostic marker (Fe-LPM). Furthermore, we adopted a multivariate Cox regression to establish a nomogram. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were also created.Results: The eight Fe-LPM (including SLC16A1-AS1, SETBP1-DT, ZNF93-AS1, SLC25A5-AS1, AC073896.2, LINC00242, PXN-AS1 and AC036176) was confirmed, and displayed a sturdy prognostic capability in the training and validation dataset. We established a nomogram based on risk score, age, pathological T stage and primary therapy outcome. GSEA revealed that several ferroptosis-related pathways are enriched in low-risk subgroups. A ceRNA network (including 4 lncRNAs, 27 miRNAs and 57 mRNAs) was also constructed to provide us some clues for finding the potential functions of these ferroptosis-related lncRNAs in PC.Conclusion: The eight Fe-LPM can be utilized for anticipating the overall survival (OS) of PC patients, which are meaningful to guiding clinical strategies of PC.

Identification of the immune cell infiltration landscape in pancreatic cancer to assist immunotherapy

2021 ◽  
Author(s):  
Zizheng Wang ◽  
Wenbo Zou ◽  
Fei Wang ◽  
Gong Zhang ◽  
Kuang Chen ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Survival Analysis ◽  
Patient Outcomes ◽  
Clustering Algorithm ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

Background: A malignant tumor's immune environment, including infiltrating immune cell status, can be critical to patient outcomes. Recent studies have shown that immune cell infiltration (ICI) in pancreatic cancer (PC) is highly correlated with the response to immunotherapy and patient prognosis. Therefore, we aimed to create an ICI score that accurately predicts patient outcomes and immunotherapeutic efficacy. Methods: The ICI statuses of patients with PC were estimated from the publicly available The Cancer Genome Atlas (TCGA) pancreatic ductal adenocarcinoma and GSE57495 gene expression datasets using two computational algorithms (CIBERSORT and ESTIMATE). ICI and transcriptome subsets were defined using a clustering algorithm, and survival analysis was also performed. Principal component analysis was used to calculate the novel ICI score, and gene set enrichment analysis was performed to identify the pathways underlying the defined clusters. The tumor mutational burden (TMB) was further explored in TCGA cohort, and survival analysis was used to assess the capability of the ICI and TMB scores to predict overall survival. Additionally, common driver gene mutations and their differential expression in the different ICI score group were investigated. Results: The ICI landscapes of 240 patients were generated using the devised algorithm, revealing three ICI and three gene clusters whose use improved the prediction of overall survival (p = 0.019 and p < 0.001, respectively). Crucial immune checkpoint genes were differentially expressed among these subtypes; the RIG-I-LIKE and NOD-LIKE receptor signaling pathways were enriched in samples with low ICI scores (p < 0.05). We also found that the TMB scores could predict survival outcomes, whereas the ICI scores also could predict prognoses independent of TMB. Notably, ICI scores could effectively predict responses to immunotherapy. KRAS, TP53, CDKN2A, SMAD4 and TTN remained the most commonly mutated genes in PC; moreover, KRAS and TP53 mutation rates were significantly different between the two ICI score groups. Conclusions: We developed a novel ICI score that could independently predict the response to immunotherapy and survival of patients with PC. Evaluation of the ICI landscape in a larger cohort could clarify the interactions between these infiltrating cells, the tumor microenvironment and response to immunotherapy.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract BackgroundHypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.MethodsDifferentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis. Then the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. CIBERSORT was used for estimating the fractions of immune cell types.ResultsA total of 397 hypoxia-related DEGs in HCC were detected and three genes (PDSS1, CDCA8 and SLC7A11) among them were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response. Meanwhile, the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.ConclusionsAltogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

scholarly journals A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

2020 ◽  
Author(s):  
Baohui Zhang ◽  
Bufu Tang ◽  
Jianyao Gao ◽  
Jiatong Li ◽  
Lingming Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Potential Biomarker

Abstract Background Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aimed to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism. Methods Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis and the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. Then the Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature and the CIBERSORT was used for estimating the fractions of immune cell types. Results A total of 397 hypoxia-related DEGs were detected and three genes (PDSS1, CDCA8 and SLC7A11) were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response and the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1. Conclusions Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

Sign in / Sign up

Export Citation Format

Share Document